Predição de novos epítopos imunogênicos de vírus Epstein-Barr através da simulação de mutações por técnicas de Bioinformática

Abstract

The Epstein-Barr virus (EBV) affects about 90% of the world’s population and has the potential to initiate many types of lymphoproliferative diseases and malignancies due to it’s immune evasion mechanism and oncogenic potential which allows the memory B cells to serve as latency compartment, making the virus being carried through all the life of the infected individual. Therefore, this study utilized computational tools such as: PyMOL, PMX, ChimeraX and others to predict new immunogenic epitopes by simulating mutations in EBV proteins, aiming to enhance understanding of the virus’s immunopathology, identify potential new therapeutic targets and contribute to scientific community. The results demonstrated, on average, 1 unusual residue for each analyzed protein, as well as several potential new immunogenic epitopes. Despite the higher number of weak bidings compared to strong bidings, it still holds importance when considering the genetic variability among individuals and binding affinities. This could later contribute to potential vaccine studies, development of new drugs, potential new biomarkers for EBV-related malignancies, and new therapies.